I have been using science and statistics since my early days as an extract home brewer. Since chilling a wort at T = 212°F to 75°F using an ice batch was a real pain, my first challenge was to determine the water volume and temperature required in order to bring my wort to pitching temperature instantaneously. I got the answer: three (3) gallons of iced water for two (2) gallons of hot wort. I found later that someone got the answer, yet it did not matter to me as I was able to find the answer myself.

For those who are not familiar with science and statistics, I did the following writing to explain what is all about.

What is the difference between voltage and current? Voltage is energy stored behind an electrical outlet and current is such voltage flowing (as current) and doing a job, like making a toast for breakfast. In other words, voltage has the capacity to do something only if its stored energy flows in a guided direction.

I compare voltage to knowledge. Someone repeats over-and-over that “Knowledge is Power”, and this is not accurate. As in the voltage analogy, its true power is manifested if and only if it is allowed to flow in a guided way. Ask yourself: can you make your savory toast for breakfast with an unplugged device or with a single piece of electrical cord? The answer is “No”. You need a plugged toaster to an outlet via an electrical cord to guide the energy in the form of current… and get your toast.

That said, my personal philosophy for many years has been the following: “Applied Knowledge is Power”. Infinitive knowledge is flowing randomly in nature and in our surroundings. Once we are able to hook up with one of those lines and successfully come up with an application, then we transform knowledge into power. In another analogy, what is a closed book if nobody opens, reads, internalizes, and applies its information? Answer: “Voltage behind an electrical outlet”… in other words, nothing.

Research is about the identification, characterization, and application of the endless the lines of knowledge randomly flowing in our nature and surroundings, which varies depending on the field of study. The main objective is to find a formula to perform predictions using paper, pencil and a calculator. Just punch in the values and get the result. For example, if you want to know is 2 + 2, you punch the values into a calculator and the result is 4. Now, imagine you have the following:

A + B = C

With the above, not only you can determine how much is 2 + 2, but with any combination of numbers such as 5 + 6 and 10 + 100 and much more. This is the advantage and versatility that a mathematical formula provides. The above mathematical formula is classified as deterministic; meaning that, no matter what, 2 + 2 always equals 4 or 1 + 1 is always 2. In other words, the result shows no variability. To me this is pretty lame and boring, as variability is the essence of life.

Imagine for a moment that you are forced to eat exactly the same food every day or if there were only one kind of color, or the same car. Life would be a so dull. Yet, we are lucky to be in a world that is inherently variable. We enjoy different food, color, aroma and other wonderful things. The downside is that when we perform research, this variability forces us to use mathematical formulas that are not deterministic and are, instead, correlations. This is where Statistics comes into play.

Statistics are a set the mathematical formulas that allow us to characterize variability. You may ask: “Why in this World you need to characterize variability?” The simple answer: “Because the blessed variability in nature is a nuisance for research purposes”. To explain this better, refer to the next example.

Imagine that you find a friend that you have not seen in long time. You meet in a restaurant to talk, but it is full of dozens of people talking at the same time, and on top of that, you have a live band playing. The jingle of people and the band is called background noise. This interferes with your main objective: to communicate and exchange information with your buddy. You don’t have control over the background noise, and as such it is a nuisance. You automatically evaluate this nuisance level and keep increasing your pitch of you voice until it is above the jingle, allowing information exchange.

This is why Research is about the characterization of variability. We want to explore and detect any important difference between the background noise and the point of interest. Also, we want to see if there is a way to reduce the background noise. Statistics is the right tool for this purpose.

Statistics holds a plethora of tools. There are two goodies: Factorial and Mixture Experimental Designs.

Going back to the toast example, lets say that you what to learn if there is a specific combination of toasting time and level of garlic on you butter (variables) that would improve the flavor profile (measured response). Now you select two (2) times (to say, 2 and 5 minutes) and amount of garlic (to say, ¼ and ½ a clove). You make all possible combinations (experiments) and taste your toast at each one. The best combination will deliver you the tastier toast.

The beauty of Factorial Designs is that you have absolute control (well, most of the time) of your variables and you can adjusted as you wish. You can go back and make endless combinations of variables and measured responses. The set of variables are independent as you can fix them independently from each other.

Mixture Experimental Designs is a completely different animal. In this case, you don’t have absolute control over your variables as they are dependent of each other. For example, let say that you want to see of certain combination of gas (to say, A and B) may improve your car gas mileage. The problem is that once select the proportion of the first, the other will follow the first one as both have to add to 100%. Now you decide to use two gallons gas as your reference volume, so the sum of both must add to two (2) gallons. If you decide that A is 1.5 gallons, automatically B will have to be 0.5 gallon so they can add to two (2) gallons.

You may say:”I cant select whatever I want, so I will use 2 gallons of A and one (1) of B. Problem is that, the gas mileage will be more, not for the combination of A and B, yet because you use more gas. Under this circumstances, the combination of A and B is said to be masked by the amount of gas.

My plan is to keep working on research to explore different aspects of the beer processing, grains and hops combinations. I may publish some of the results even better, served as very good beer.